Automated Scene Understanding

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-12-C-0263
Agency Tracking Number: O2-1225
Amount: $739,954.00
Phase: Phase II
Program: SBIR
Awards Year: 2012
Solicitation Year: 2010
Solicitation Topic Code: OSD10-L04
Solicitation Number: 2010.2
Small Business Information
ObjectVideo
11600 Sunrise Valley Drive, Suite # 290, Reston, VA, -
DUNS: 038732173
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Atul Kanaujia
 Principal Investigator
 (703) 654-9300
 akanaujia@objectvideo.com
Business Contact
 Paul Brewer
Title: VP, New Technology
Phone: (703) 725-3084
Email: pbrewer@objectvideo.com
Research Institution
 Stub
Abstract
Automatic extraction and representation of visual concepts and semantic information in scenes is a desired capability in surveillance operations. In this effort we will advance the foundations of data representation and fusion at various levels of abstraction. We target the problem of complex event recognition in network information environment, where lack of effective visual processing tools and incomplete domain knowledge frequently cause uncertainty in the datasets and consequently, in the visual primitives extracted from it. We employ Markov Logic Network (MLN) to address the task of reasoning under uncertainty. In Phase I, we demonstrated use of MLN as a domain knowledge representation language that can be used for inferring complex events in real world. In Phase II, our emphasis will be on developing algorithms to fuse data from multiple sources, perform reasoning in the presence of incomplete data, and transfer learning for domain adaptation. At the visual processing level, transfer learning will enable zero-shot recognition of unknown classes. At the decision level, transfer learning is applied to MLN to automatically infer rules for related but unseen domains. Technical claims made during the study will be justified using rigorous testing and comparison with other state-of-the-art methods on publically available datasets.

* information listed above is at the time of submission.

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